Senior Machine Learning Data Scientist

📋 Description

• • Lead the end-to-end lifecycle of fraud-detection and risk-prediction models that protect millions of Extend customers and merchants every day. From exploratory data analysis to model deployment and monitoring, you will own the technical roadmap and deliver measurable reductions in fraudulent claims and chargebacks.
• • Architect, train, and tune state-of-the-art algorithms—gradient-boosted trees, deep neural networks, graph-based anomaly detection, and sequence models—to surface subtle fraud patterns in real time transaction streams. You will balance precision and recall to minimize false positives that could harm legitimate customers while maximizing fraud capture.
• • Build robust, scalable ML pipelines on AWS (SageMaker, Lambda, Kinesis, S3) that process terabytes of structured and unstructured data daily. Your pipelines will be versioned, tested, and monitored with CI/CD best practices so that new models can be promoted to production safely and frequently.
• • Partner directly with Product Managers to translate business objectives into ML problems. You will scope experiments, define success metrics, and present findings to executive stakeholders, ensuring that every model drives clear ROI for Extend’s post-purchase protection products.
• • Collaborate with Engineering to integrate models into microservices and event-driven architectures. You will write production-grade Python, design feature stores, and implement real-time inference endpoints with sub-100 ms latency to support checkout and claims experiences.
• • Establish rigorous A/B testing and counterfactual evaluation frameworks that quantify model impact on revenue, customer satisfaction, and operational cost. You will continuously iterate based on live performance data and feedback loops from Operations and Customer Support teams.
• • Mentor junior data scientists and ML engineers through code reviews, design sessions, and knowledge-sharing talks. You will cultivate a culture of scientific rigor, reproducibility, and ethical AI across the Risk & Fraud organization.
• • Stay ahead of emerging fraud vectors—synthetic identity, account takeover, refund abuse—and proactively research novel techniques such as federated learning, self-supervised representation learning, and transformer-based sequence modeling to keep Extend’s defenses best-in-class.
• • Contribute to Extend’s data governance and privacy initiatives, ensuring that all models comply with GDPR, CCPA, and industry standards. You will implement differential privacy, model explainability dashboards, and bias audits to maintain customer trust and regulatory compliance.
• • Drive strategic data acquisition partnerships and third-party data enrichment strategies that expand the feature space for risk modeling. You will evaluate external vendors, negotiate SLAs, and integrate new data sources to improve model lift and coverage.
• • Champion a metrics-driven culture by publishing internal white-papers, presenting at meetups, and filing patents on novel fraud-detection techniques. Your thought leadership will position Extend as an innovator in the insure-tech and fintech ecosystems.